Sentence examples for sample settings from inspiring English sources

This does not take into account feature-selection, which is commonplace in high-dimensional and small sample settings.

A considerable number, 19 records, makes use of comparisons of student results or motivation changes within small sample settings.

A number of references emphasize that the restricted-maximum-likelihood method for estimation of regression weights works well in small sample settings, and there are a number of exemplary multilevel applications that refer to small sample sizes at Level 2 (N = 15 20) (e.g., Kenward and Roger 1997; McNeish and Stapleton 2016; Skene and Kenward 2010; Stegmueller 2013).

Although this study must be viewed simply as an exercise under particular worst sample settings, our results suggest that, at least, those single nucleotide polymorphisms (SNPs) with large allele frequency differences among Iberians and Northwest Africans might be at risk of being detected as false positives in association studies conducted in Spanish populations.

The instability is even worse in small sample settings (Braga-Neto and Dougherty, 2004).

The haplotype-based variant detection approach can also be conducted in either single or multi sample settings.

Roy Furchgott Correction: January 8 , 2004 Thursday An article in Circuits last Thursday about copying LP's onto CD's misstated the sampling settings at which the software captures the best sound from analog recordings.

This paper concerns the classical problem of determining the performance of error estimators in small-sample settings under a Gaussianity parametric assumption.

However, the problem of error estimation for these classification rules, particularly for bagging under the small-sample settings prevalent in genomics and proteomics, is not well understood.

BIC is often employed in large-sample settings where the modeling objective is descriptive, since it eliminates variables corresponding to negligible effects that are statistically significant merely because of the sample size.

However, in practical real-world problems, the underlying probability model is unknown, and thus we arrive at the fundamental issue of how to find a good prediction error estimator in small-sample settings [8, 9].